Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations3732
Missing cells0
Missing cells (%)0.0%
Duplicate rows2
Duplicate rows (%)0.1%
Total size in memory861.5 KiB
Average record size in memory236.4 B

Variable types

Categorical1
Text1
Numeric9
Boolean1

Alerts

Dataset has 2 (0.1%) duplicate rowsDuplicates
discountPercent has 1178 (31.6%) zeros Zeros
availableQuantity has 453 (12.1%) zeros Zeros
total_revenue has 454 (12.2%) zeros Zeros
discountValue has 1169 (31.3%) zeros Zeros

Reproduction

Analysis started2025-07-14 14:41:46.754627
Analysis finished2025-07-14 14:42:11.130104
Duration24.38 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Category
Categorical

Distinct14
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size261.2 KiB
Cooking Essentials
514 
Munchies
514 
Packaged Food
388 
Ice Cream & Desserts
388 
Chocolates & Candies
388 
Other values (9)
1540 

Length

Max length21
Median length19
Mean length14.628349
Min length8

Characters and Unicode

Total characters54593
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFruits & Vegetables
2nd rowFruits & Vegetables
3rd rowFruits & Vegetables
4th rowFruits & Vegetables
5th rowFruits & Vegetables

Common Values

ValueCountFrequency (%)
Cooking Essentials 514
13.8%
Munchies 514
13.8%
Packaged Food 388
10.4%
Ice Cream & Desserts 388
10.4%
Chocolates & Candies 388
10.4%
Personal Care 344
9.2%
Paan Corner 344
9.2%
Home & Cleaning 194
 
5.2%
Biscuits 147
 
3.9%
Dairy, Bread & Batter 129
 
3.5%
Other values (4) 382
10.2%

Length

2025-07-14T20:12:11.724118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1352
 
15.7%
cooking 514
 
6.0%
munchies 514
 
6.0%
essentials 514
 
6.0%
packaged 388
 
4.5%
food 388
 
4.5%
ice 388
 
4.5%
cream 388
 
4.5%
desserts 388
 
4.5%
chocolates 388
 
4.5%
Other values (19) 3384
39.3%

Most occurring characters

ValueCountFrequency (%)
e 6442
 
11.8%
s 5138
 
9.4%
4874
 
8.9%
a 4793
 
8.8%
o 3462
 
6.3%
n 3447
 
6.3%
i 2800
 
5.1%
r 2761
 
5.1%
C 2560
 
4.7%
t 2041
 
3.7%
Other values (22) 16275
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54593
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6442
 
11.8%
s 5138
 
9.4%
4874
 
8.9%
a 4793
 
8.8%
o 3462
 
6.3%
n 3447
 
6.3%
i 2800
 
5.1%
r 2761
 
5.1%
C 2560
 
4.7%
t 2041
 
3.7%
Other values (22) 16275
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54593
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6442
 
11.8%
s 5138
 
9.4%
4874
 
8.9%
a 4793
 
8.8%
o 3462
 
6.3%
n 3447
 
6.3%
i 2800
 
5.1%
r 2761
 
5.1%
C 2560
 
4.7%
t 2041
 
3.7%
Other values (22) 16275
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54593
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6442
 
11.8%
s 5138
 
9.4%
4874
 
8.9%
a 4793
 
8.8%
o 3462
 
6.3%
n 3447
 
6.3%
i 2800
 
5.1%
r 2761
 
5.1%
C 2560
 
4.7%
t 2041
 
3.7%
Other values (22) 16275
29.8%

name
Text

Distinct1681
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Memory size334.4 KiB
2025-07-14T20:12:12.190853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length95
Median length68
Mean length33.938103
Min length4

Characters and Unicode

Total characters126657
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique467 ?
Unique (%)12.5%

Sample

1st rowOnion
2nd rowTomato Hybrid
3rd rowTender Coconut
4th rowCoriander Leaves
5th rowLadies Finger
ValueCountFrequency (%)
764
 
3.8%
masala 247
 
1.2%
popular 191
 
1.0%
essentials 186
 
0.9%
noodles 163
 
0.8%
chicken 155
 
0.8%
ching's 151
 
0.8%
cheese 135
 
0.7%
secret 133
 
0.7%
sauce 130
 
0.6%
Other values (1869) 17746
88.7%
2025-07-14T20:12:12.941858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16601
 
13.1%
a 11161
 
8.8%
e 10625
 
8.4%
i 7321
 
5.8%
r 6931
 
5.5%
o 6309
 
5.0%
t 5827
 
4.6%
n 5528
 
4.4%
l 5452
 
4.3%
s 5194
 
4.1%
Other values (71) 45708
36.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 126657
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
16601
 
13.1%
a 11161
 
8.8%
e 10625
 
8.4%
i 7321
 
5.8%
r 6931
 
5.5%
o 6309
 
5.0%
t 5827
 
4.6%
n 5528
 
4.4%
l 5452
 
4.3%
s 5194
 
4.1%
Other values (71) 45708
36.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 126657
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
16601
 
13.1%
a 11161
 
8.8%
e 10625
 
8.4%
i 7321
 
5.8%
r 6931
 
5.5%
o 6309
 
5.0%
t 5827
 
4.6%
n 5528
 
4.4%
l 5452
 
4.3%
s 5194
 
4.1%
Other values (71) 45708
36.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 126657
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
16601
 
13.1%
a 11161
 
8.8%
e 10625
 
8.4%
i 7321
 
5.8%
r 6931
 
5.5%
o 6309
 
5.0%
t 5827
 
4.6%
n 5528
 
4.4%
l 5452
 
4.3%
s 5194
 
4.1%
Other values (71) 45708
36.1%

mrp
Real number (ℝ)

Distinct266
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15680.118
Minimum0
Maximum260000
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size29.3 KiB
2025-07-14T20:12:13.157650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2300
Q16000
median11000
Q320000
95-th percentile41000
Maximum260000
Range260000
Interquartile range (IQR)14000

Descriptive statistics

Standard deviation16088.808
Coefficient of variation (CV)1.0260642
Kurtosis40.399968
Mean15680.118
Median Absolute Deviation (MAD)6200
Skewness4.4372905
Sum58518200
Variance2.5884973 × 108
MonotonicityNot monotonic
2025-07-14T20:12:13.442440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6000 122
 
3.3%
5500 94
 
2.5%
12000 89
 
2.4%
5000 85
 
2.3%
7500 80
 
2.1%
11000 79
 
2.1%
9900 77
 
2.1%
15000 76
 
2.0%
3000 75
 
2.0%
4000 72
 
1.9%
Other values (256) 2883
77.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
1000 38
1.0%
1100 3
 
0.1%
1200 9
 
0.2%
1300 2
 
0.1%
1400 2
 
0.1%
1500 56
1.5%
1600 2
 
0.1%
1700 4
 
0.1%
1800 11
 
0.3%
ValueCountFrequency (%)
260000 2
 
0.1%
154900 2
 
0.1%
145000 2
 
0.1%
125000 2
 
0.1%
124000 2
 
0.1%
120000 2
 
0.1%
109900 6
0.2%
105000 6
0.2%
100500 2
 
0.1%
92500 2
 
0.1%

discountPercent
Real number (ℝ)

Zeros 

Distinct42
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6170954
Minimum0
Maximum51
Zeros1178
Zeros (%)31.6%
Negative0
Negative (%)0.0%
Memory size29.3 KiB
2025-07-14T20:12:13.709699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q310
95-th percentile25
Maximum51
Range51
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.211733
Coefficient of variation (CV)1.2093498
Kurtosis6.5885463
Mean7.6170954
Median Absolute Deviation (MAD)6
Skewness2.2120853
Sum28427
Variance84.856025
MonotonicityNot monotonic
2025-07-14T20:12:13.961578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 1178
31.6%
10 468
 
12.5%
5 226
 
6.1%
6 213
 
5.7%
9 201
 
5.4%
2 186
 
5.0%
7 127
 
3.4%
15 125
 
3.3%
4 122
 
3.3%
20 113
 
3.0%
Other values (32) 773
20.7%
ValueCountFrequency (%)
0 1178
31.6%
1 44
 
1.2%
2 186
 
5.0%
3 86
 
2.3%
4 122
 
3.3%
5 226
 
6.1%
6 213
 
5.7%
7 127
 
3.4%
8 69
 
1.8%
9 201
 
5.4%
ValueCountFrequency (%)
51 3
 
0.1%
50 55
1.5%
49 3
 
0.1%
46 2
 
0.1%
45 2
 
0.1%
43 4
 
0.1%
40 10
 
0.3%
35 2
 
0.1%
33 2
 
0.1%
32 2
 
0.1%

availableQuantity
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0085745
Minimum0
Maximum6
Zeros453
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size29.3 KiB
2025-07-14T20:12:14.143516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q36
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2035111
Coefficient of variation (CV)0.54969943
Kurtosis-1.0462062
Mean4.0085745
Median Absolute Deviation (MAD)1
Skewness-0.66641924
Sum14960
Variance4.8554612
MonotonicityNot monotonic
2025-07-14T20:12:14.293428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 1620
43.4%
0 453
 
12.1%
4 414
 
11.1%
3 351
 
9.4%
5 348
 
9.3%
1 301
 
8.1%
2 245
 
6.6%
ValueCountFrequency (%)
0 453
 
12.1%
1 301
 
8.1%
2 245
 
6.6%
3 351
 
9.4%
4 414
 
11.1%
5 348
 
9.3%
6 1620
43.4%
ValueCountFrequency (%)
6 1620
43.4%
5 348
 
9.3%
4 414
 
11.1%
3 351
 
9.4%
2 245
 
6.6%
1 301
 
8.1%
0 453
 
12.1%

discountedSellingPrice
Real number (ℝ)

Distinct350
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14192.835
Minimum0
Maximum139900
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size29.3 KiB
2025-07-14T20:12:14.944912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2055
Q15500
median10400
Q318400
95-th percentile37145
Maximum139900
Range139900
Interquartile range (IQR)12900

Descriptive statistics

Standard deviation13850.726
Coefficient of variation (CV)0.97589568
Kurtosis16.600296
Mean14192.835
Median Absolute Deviation (MAD)5700
Skewness3.2020841
Sum52967660
Variance1.9184262 × 108
MonotonicityNot monotonic
2025-07-14T20:12:15.194243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1500 59
 
1.6%
2500 59
 
1.6%
5500 56
 
1.5%
6000 47
 
1.3%
4400 47
 
1.3%
7500 45
 
1.2%
4500 44
 
1.2%
9900 43
 
1.2%
8000 40
 
1.1%
1000 39
 
1.0%
Other values (340) 3253
87.2%
ValueCountFrequency (%)
0 1
 
< 0.1%
900 3
 
0.1%
1000 39
1.0%
1100 3
 
0.1%
1200 9
 
0.2%
1300 3
 
0.1%
1400 1
 
< 0.1%
1500 59
1.6%
1600 5
 
0.1%
1700 12
 
0.3%
ValueCountFrequency (%)
139900 2
0.1%
130500 2
0.1%
124000 2
0.1%
114300 2
0.1%
105000 2
0.1%
103900 2
0.1%
103500 2
0.1%
99900 2
0.1%
98400 2
0.1%
92500 2
0.1%

weightInGms
Real number (ℝ)

Distinct158
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean387.84378
Minimum0
Maximum10000
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size29.3 KiB
2025-07-14T20:12:15.461193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1100
median225
Q3450
95-th percentile1000
Maximum10000
Range10000
Interquartile range (IQR)350

Descriptive statistics

Standard deviation678.09651
Coefficient of variation (CV)1.7483753
Kurtosis66.049269
Mean387.84378
Median Absolute Deviation (MAD)155
Skewness6.9886891
Sum1447433
Variance459814.88
MonotonicityNot monotonic
2025-07-14T20:12:15.733685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 410
 
11.0%
500 388
 
10.4%
100 285
 
7.6%
1000 213
 
5.7%
400 213
 
5.7%
250 171
 
4.6%
50 124
 
3.3%
150 95
 
2.5%
300 93
 
2.5%
60 84
 
2.3%
Other values (148) 1656
44.4%
ValueCountFrequency (%)
0 4
 
0.1%
1 2
 
0.1%
5 5
 
0.1%
8 4
 
0.1%
9 2
 
0.1%
10 7
 
0.2%
14 2
 
0.1%
15 22
0.6%
16 3
 
0.1%
18 2
 
0.1%
ValueCountFrequency (%)
10000 4
 
0.1%
5000 42
1.1%
4000 3
 
0.1%
3000 12
 
0.3%
2000 12
 
0.3%
1900 1
 
< 0.1%
1500 8
 
0.2%
1200 15
 
0.4%
1160 26
0.7%
1100 3
 
0.1%

outOfStock
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
False
3279 
True
453 
ValueCountFrequency (%)
False 3279
87.9%
True 453
 
12.1%
2025-07-14T20:12:15.924863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

quantity
Real number (ℝ)

Distinct143
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean213.2709
Minimum0
Maximum1500
Zeros6
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size29.3 KiB
2025-07-14T20:12:16.095238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q150
median186
Q3340
95-th percentile500
Maximum1500
Range1500
Interquartile range (IQR)290

Descriptive statistics

Standard deviation194.73098
Coefficient of variation (CV)0.91306866
Kurtosis1.9359411
Mean213.2709
Median Absolute Deviation (MAD)136
Skewness1.1624395
Sum795927
Variance37920.153
MonotonicityNot monotonic
2025-07-14T20:12:16.646773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 417
 
11.2%
500 400
 
10.7%
1 285
 
7.6%
100 284
 
7.6%
400 206
 
5.5%
250 166
 
4.4%
50 123
 
3.3%
300 93
 
2.5%
150 92
 
2.5%
20 80
 
2.1%
Other values (133) 1586
42.5%
ValueCountFrequency (%)
0 6
 
0.2%
1 285
7.6%
2 33
 
0.9%
3 21
 
0.6%
4 33
 
0.9%
5 49
 
1.3%
6 40
 
1.1%
7 21
 
0.6%
8 4
 
0.1%
9 4
 
0.1%
ValueCountFrequency (%)
1500 2
 
0.1%
1200 3
 
0.1%
1100 3
 
0.1%
1000 1
 
< 0.1%
975 4
0.1%
950 5
0.1%
900 8
0.2%
875 3
 
0.1%
860 1
 
< 0.1%
850 3
 
0.1%

price_per_kg
Real number (ℝ)

Distinct961
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Infinite4
Infinite (%)0.1%
Meaninf
Minimum0
Maximuminf
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size29.3 KiB
2025-07-14T20:12:16.938526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9155.2759
Q124400
median46450
Q382000
95-th percentile256403.45
Maximuminf
Rangeinf
Interquartile range (IQR)57600

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Meaninf
Median Absolute Deviation (MAD)26100
Skewnessnan
Suminf
Variancenan
MonotonicityNot monotonic
2025-07-14T20:12:17.211864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25000 66
 
1.8%
50000 44
 
1.2%
100000 31
 
0.8%
75000 26
 
0.7%
47000 24
 
0.6%
20000 24
 
0.6%
117000 24
 
0.6%
80000 24
 
0.6%
60000 23
 
0.6%
28500 22
 
0.6%
Other values (951) 3424
91.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
1724.137931 1
 
< 0.1%
1900 3
0.1%
2100 1
 
< 0.1%
2400 2
0.1%
2413.793103 1
 
< 0.1%
2600 1
 
< 0.1%
2800 2
0.1%
2900 1
 
< 0.1%
3000 1
 
< 0.1%
ValueCountFrequency (%)
inf 4
0.1%
12000000 2
0.1%
3380000 2
0.1%
2380000 2
0.1%
2043181.818 2
0.1%
1696666.667 2
0.1%
1664814.815 2
0.1%
1516666.667 2
0.1%
1419230.769 2
0.1%
1350000 2
0.1%

total_revenue
Real number (ℝ)

Zeros 

Distinct572
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60103.982
Minimum0
Maximum839400
Zeros454
Zeros (%)12.2%
Negative0
Negative (%)0.0%
Memory size29.3 KiB
2025-07-14T20:12:17.443312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114000
median39500
Q382200
95-th percentile178200
Maximum839400
Range839400
Interquartile range (IQR)68200

Descriptive statistics

Standard deviation74065.677
Coefficient of variation (CV)1.2322924
Kurtosis26.481113
Mean60103.982
Median Absolute Deviation (MAD)30100
Skewness3.8897315
Sum2.2430806 × 108
Variance5.4857245 × 109
MonotonicityNot monotonic
2025-07-14T20:12:17.695357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 454
 
12.2%
15000 33
 
0.9%
59400 30
 
0.8%
9000 27
 
0.7%
48600 27
 
0.7%
26400 26
 
0.7%
45000 26
 
0.7%
27000 26
 
0.7%
54000 26
 
0.7%
36000 24
 
0.6%
Other values (562) 3033
81.3%
ValueCountFrequency (%)
0 454
12.2%
1000 9
 
0.2%
1800 1
 
< 0.1%
2000 16
 
0.4%
2300 3
 
0.1%
2500 11
 
0.3%
2700 3
 
0.1%
2900 1
 
< 0.1%
3000 9
 
0.2%
3200 2
 
0.1%
ValueCountFrequency (%)
839400 2
0.1%
783000 2
0.1%
744000 2
0.1%
685800 2
0.1%
623400 2
0.1%
621000 2
0.1%
599400 2
0.1%
590400 2
0.1%
555000 2
0.1%
539400 2
0.1%

discountValue
Real number (ℝ)

Zeros 

Distinct104
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1487.283
Minimum0
Maximum120100
Zeros1169
Zeros (%)31.3%
Negative0
Negative (%)0.0%
Memory size29.3 KiB
2025-07-14T20:12:17.943791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median500
Q31600
95-th percentile5800
Maximum120100
Range120100
Interquartile range (IQR)1600

Descriptive statistics

Standard deviation4066.5619
Coefficient of variation (CV)2.7342221
Kurtosis433.07718
Mean1487.283
Median Absolute Deviation (MAD)500
Skewness16.880563
Sum5550540
Variance16536926
MonotonicityNot monotonic
2025-07-14T20:12:18.204424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1169
31.3%
100 193
 
5.2%
500 170
 
4.6%
600 152
 
4.1%
300 133
 
3.6%
700 125
 
3.3%
400 112
 
3.0%
800 108
 
2.9%
900 100
 
2.7%
200 98
 
2.6%
Other values (94) 1372
36.8%
ValueCountFrequency (%)
0 1169
31.3%
100 193
 
5.2%
200 98
 
2.6%
250 2
 
0.1%
300 133
 
3.6%
400 112
 
3.0%
500 170
 
4.6%
600 152
 
4.1%
700 125
 
3.3%
750 2
 
0.1%
ValueCountFrequency (%)
120100 2
0.1%
70000 2
0.1%
26400 3
0.1%
25000 2
0.1%
22500 2
0.1%
21600 2
0.1%
20000 4
0.1%
17800 2
0.1%
15000 2
0.1%
14800 4
0.1%

Interactions

2025-07-14T20:12:08.559381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:48.968383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:52.826108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:55.411458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:57.725464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:59.913798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:02.202863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:04.148973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:06.740040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:08.769294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:50.005525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:53.028121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:55.722369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:57.993387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:00.300541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:02.413725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:04.738560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:06.940187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:08.961238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:50.358055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:53.208123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:55.945167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:58.234334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:00.719470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:02.620821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:04.960335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:07.141490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:09.174430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:50.707216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:53.777472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:56.198122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:58.520900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:00.935470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:02.836203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:05.177015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:07.342057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:09.412081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:51.085825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:54.026161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:56.459211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:58.825484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:01.149548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:03.069305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:05.407687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:07.566015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:09.612296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:51.343317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:54.345503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:56.698207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:59.044654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:01.346213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:03.268836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:05.884638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:07.757478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:09.837776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:51.721541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:54.647591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:57.048380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:59.259220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:01.585505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:03.507815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:06.101205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:07.975320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:10.054864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:52.192906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:54.923718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:57.281936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:59.487151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:01.781764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:03.722828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:06.308623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:08.175145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:10.256893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:52.476456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:55.161869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:57.500194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:11:59.693093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:01.984747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:03.939627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:06.525637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:12:08.360158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Missing values

2025-07-14T20:12:10.567189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-14T20:12:10.843895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CategorynamemrpdiscountPercentavailableQuantitydiscountedSellingPriceweightInGmsoutOfStockquantityprice_per_kgtotal_revenuediscountValue
0Fruits & VegetablesOnion250016321001000False12100.0000006300400
1Fruits & VegetablesTomato Hybrid420016335001000False13500.00000010500700
2Fruits & VegetablesTender Coconut5100153430058False174137.93103412900800
3Fruits & VegetablesCoriander Leaves20001531700100False10017000.0000005100300
4Fruits & VegetablesLadies Finger14001431200250False2504800.0000003600200
5Fruits & VegetablesPotato350017329001000False12900.0000008700600
6Fruits & VegetablesLemon75001636300200False20031500.000000189001200
7Fruits & VegetablesWatermelon5800153490058False184482.75862114700900
8Fruits & VegetablesCapsicum Green23001731900250False2507600.0000005700400
9Fruits & VegetablesChilli Green19001531600100False10016000.0000004800300
CategorynamemrpdiscountPercentavailableQuantitydiscountedSellingPriceweightInGmsoutOfStockquantityprice_per_kgtotal_revenuediscountValue
3722Health & HygieneEno Cooling Sachets - Cool Mint, 6 Pieces Carton540011048005True5960000.0000000600
3723Health & HygieneWhisper Choice Sanitary Pad3200902900406True77142.8571430300
3724Health & HygieneWhisper Bindazzz Nights XL + Sanitary Pad9000009000406True722167.48768500
3725Health & HygieneGillette Venus Close & Clean Razor24900702300058True1396551.72413801900
3726Health & HygieneWhisper Choice Regular Sanitary Pads900030870058True20150000.0000000300
3727Health & HygieneStayfree Secure Dry Cover Extra Large Sanitary Pad4200204100406True710098.5221670100
3728Health & HygieneDabur Honitus Herbal Cough Remedy Ayurvedic Syrup105001508900100True10089000.00000001600
3729Health & HygieneWhisper Bindazzz Night Sanitary Pads XL Plus185000018500870True1521264.36781600
3730Health & HygieneFine Life Cotton Balls600000600050True50120000.00000000
3731Health & HygieneDettol Antiseptic Liquid300000300060True6050000.00000000

Duplicate rows

Most frequently occurring

CategorynamemrpdiscountPercentavailableQuantitydiscountedSellingPriceweightInGmsoutOfStockquantityprice_per_kgtotal_revenuediscountValue# duplicates
0Paan CornerListerine Cool Mint Mouthwash - Mild Taste1500010613500250False25054000.08100015002
1Personal CareListerine Cool Mint Mouthwash - Mild Taste1500010613500250False25054000.08100015002